Reconstruction of Sound Field through Diffusion Models

Autor:	Miotello, Federico, Comanducci, Luca, Pezzoli, Mirco, Bernardini, Alberto, Antonacci, Fabio, Sarti, Augusto
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Reconstructing the sound field in a room is an important task for several applications, such as sound control and augmented (AR) or virtual reality (VR). In this paper, we propose a data-driven generative model for reconstructing the magnitude of acoustic fields in rooms with a focus on the modal frequency range. We introduce, for the first time, the use of a conditional Denoising Diffusion Probabilistic Model (DDPM) trained in order to reconstruct the sound field (SF-Diff) over an extended domain. The architecture is devised in order to be conditioned on a set of limited available measurements at different frequencies and generate the sound field in target, unknown, locations. The results show that SF-Diff is able to provide accurate reconstructions, outperforming a state-of-the-art baseline based on kernel interpolation. Comment: Accepted for publication at ICASSP 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.08821 Zobrazit plný text záznamu View this record from Arxiv